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Intro to Qualitative Research

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Jermaine Ravalier

on 20 November 2013

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Transcript of Intro to Qualitative Research

Intro to Qualitative Research

Dr Jermaine M Ravalier
L4 Research Methods

Learning Outcomes
Understand what qualitative methods are

Understand and evaluate the types of qualitative data collection

Understand the philosophical underpinning to quant v qual arguments

Disadvantages of Quantitative Methods
As a mixed-methods researcher, I can appreciate both the good and bad points of quantitative research

Even though you do all of these stats – you can never prove anything!!!
Reductionist approaches, i.e. breaking things down into smaller pieces, have been shown to be inadequate.
They collect a really narrow dataset, i.e. they can only find what they want to find.
Don’t describe the whole of the situation/person etc.

These are just a few – there are loads of other disadvantages of quantitative methods. So…we need another way of doing things.
Psychological Research is Often Classified as Quant OR Qual
Qualitative Characteristics
Characteristics of qualitative research – what does it look like?

The purpose is on understanding the whole people and situations, not just small bits of it.
Oriented toward discovery, rather than proof.
Uses subjective data.
Takes the context of the research into account.
Is based on textual, rather than numeric, data.

Defining Qualitative Research
Qualitative: gather in-depth understanding of human behaviour and reasons behind behaviours.
Remember: psychology is the study of human behaviour.
Perhaps the most important point?
In-depth information gives you more of an explanation as to why people do the things that they do.
Battles Within Psychology
Now, Psychological RM may seem a little boring, but believe it or not…battles are waging!!

Traditionally, Psychology is a science – hence all the numbers and statistics.

Q) Is it right to reduce thoughts, feelings, etc to numbers to be statistically analysed?
Psychological Research is Often Classified as Quant OR Qual
Until the last 3-4 decades, quantitative methods dominated social science research

Quantitative research
asks ‘how many/often’, and therefore works with numbers & stats (fear of stats shouldn’t be a reason NOT to do quant research…).

Qualitative research
asks ‘why/how’, and therefore (usually) works with text (more exploratory).
Types of Qualitative Data Collection
What types of qualitative data collection can you think of...
Summarising the Debate
This video nicely emphasises the way many see ual and quant as completely separate approaches.
However, later we will discover that the differences aren't really that broad...
Interviews as Qualitative Data Collection
Interviews & Interviewing
Today we will focus on Interviewing as a qualitative data collection technique

Interviewing: “a conversation with a purpose”
(Berg, 2001, pp. 66).

Interviews are used in psychological and other social science research and practice every day:
Social psychology (e.g. Milgram)
Developmental psychology (e.g. Corsaro)
‘Abnormal’ psychology (e.g. Mental health interviews)
Organisational psychology /individual differences (e.g. Personnel selection and assessment)

Berg, B.L. (2001), Qualitative Research Methods for the Social Sciences, London: Allyn & Bacon
“a conversation with a purpose”

Interviews can be thought of as existing on a kind of continuum, from structured to unstructured.
Structured Unstructured
Most commonly used in Psychological research – think of these as a mix of semi-structured and structured interviews

The interviewer is guided by the interview questions (as in structured) but not bound by them (as in unstructured) – hence the name ‘semi-structured’.
Interviewer starts with a number of questions to start discussions.
Researcher then probes answers to each question.
Therefore the interview questions are maleable to what the researcher finds interesting in the interviewee’s answers.
The best examples of unstructured interviews are the everyday conversations that we have

There are even researchers who specialise in analysing these everyday conversations!
Brilliant for analysing naturalistic discourse (dialogue/discussions) between people.
No schedule of questioning – complete free-flowing discourse.
These resemble a spoken questionnaire

Interview questions are set with no deviation from them.
Participant responses could either be open or closed.
E.g. A telephone questionnaire
E.g. Text message questionnaire
All participants receive standardised questions.
Q) Does this remind you of a quantitative approach?
Structured Interviews
Semi-Structured Interviews
Unstructured Interviews
When to Use Interviews
As highlighted earlier, interviews are used in a variety of settings and for differing reasons

These settings and reasons include:
As the main method of data collection (remember: know; do; think; feel; experience).
To identify new areas for exploration.
As part of a pilot study.
To confirm findings of a quantitative study.
To further explore findings of a quantitative study.
What are the advantages and disadvantages of using interviews?
Evaluation of Interviews
Take Home Message
Despite the conflict and innate discussions of ‘qualitative vs quantitative’ research, the two are in many ways similar

Systematic approach to both

Validity and reliability / trustworthiness

Research questions / hypotheses
Coolican, H. (2009), Research Methods and Statistics in Psychology, 5th Ed. London: Routledge

Chapter 10:
Qualitative approaches in psychology
Until the last 4/5 decades, quantitative methods have dominated.
Interviews, focus groups, observations, diaries, secondary data…
Often analysed using quantitative methods (those darned statistics!!!)
About Me...
Dr Jermaine M Ravalier

Lecturer in Qualitative Research Methods
Teaching & research interests:
Occupational psychology
Social psychology

What is qualitative research?
Qual vs Quant: Paradigm wars
Types of qualitative methodology
Reliability & validity in qualitative research
Content Analysis & Thematic Analysis
The Differences Between Quant and Qual Research
Qualitative and quantitative research are separate and different, leading to arguments among researchers for decades (Patton, 1990)

Epistemology: the roots of the conflict
Epistemology reflects how different approaches know what they know.
How qualitative researchers know stuff = interpretivism
How quantitative researchers know stuff = positivism
The differences are marked – many researchers will do one, and not go near the other

Positivist researchers decry a lack of scientific methods in interpretivist research.
Interpretivist researchers decry a lack of depth and personal understanding in positivist research

Rigor in Qualitative Research
This is the qualitative version of validity and reliability
In quant research:
Validity = does it measure what it’s meant to measure?
Reliability = will the results be consistent?

Is an important part of what makes Positivist research ‘scientific’.
Is also one of the ways in which Qual research has been put down over the years.

Difficult to ascertain a measure of validity and reliability for all qualitative research

However, certain things can be done in all qualitative research to ensure valid results.
Validity is known as: quality / rigor / trustworthiness in qualitative research.
e.g. see:
Golafshani, N. (2003). Understanding Validity and Reliability in Qualitative Research, The Qualitative Report, 8 (4), pp. 597-607
Cho, J., & Tren, A. (2006). Validity in Qualitative Research Revisited, Qualitative Research, 6 (3), pp. 319-340

Ensuring Validity of Qualitative Research
1. Triangulation – collecting data from various sources.
2. Comparing researchers’ coding – have more than one researcher code. Are the outcomes similar?
3. Participant feedback – also known as ‘member checking’.
4. Disconfirming case analysis – after coding, look for ‘deviant cases’ (similar to falsification in quant research).
5. Transparency – helps to show readers that a rigorous process was used.

Intro to Qualitative Analysis
Over the next few weeks, we’ll focus on a few different examples:

1. Content analysis
2. Thematic analysis
3. Grounded theory
4. Interpretive Phenomenological Analysis (IPA)
Content Analysis: What is it?
“A research technique for the objective, systematic, and quantitative description…of communications” (Berelson, 1974)

A research tool which focuses on the actual data, rather than anything else about it (e.g. context etc).
Determines importance of terms/concepts/themes by the number of times they come up.
Texts include interview data, book chapters, news articles etc.
Therefore it’s quite good for secondary data analysis.

Content Analysis: How to do it
There are certain steps you need to take when doing content analysis

1. Formulate research questions/objectives.
Important as to how, and what, is coded.
Tells you what the analysis is going to be based on.
2. Transcribe and gain closeness to data.
Once interviews are done, need to get ‘close’ to the data. Read, re-read, compare transcription to the recording.
3. Design content (subject) categories.
Will come from research questions (and therefore literature review).
Are coding against these specific categories.
4. Prepare a coding schedule.
How are you going to code – line by line, sentence by sentence, paragraph by paragraph?
5. Carry out the coding.
Using schedule, compare the text to the categories you designed in Step 3.
6. Produce the report.
Title, description, number of times, percentage, table? Bar chart?

Content Analysis: Drawbacks of Use
Time consuming!!
Turns qual into quant (just counts words…)
Disregards context (as you’re just counting words.
Difficult to automate, unlike quan methods.

Thematic Analysis: What is it?

A method for identifying, analysing and reporting patterns (or themes) within data

Seek to identify themes which represent the data collected in the form of interviews, focus groups and other textual material (Howitt & Cramer, 2011).
End goal: collate themes which best represent data.
Does not depend on theoretical background and underpinnings.
Braun, V., & Clarke, V. (2006). “Using Thematic Analysis in Psychology”, Qualitative Research in Psychology, 3, pp. 77-101
Doing Thematic Analysis
Braun & Clarke (2006) are eminent authors, and published a 6-step method to thematic analysis (see handout too)

1. Familiarise yourself with the data.
Watch clip, listen to it, transcribe, read, re-read, note initial ideas.
2. Generate initial ‘codes’.
Code interesting features of the data systematically across whole data set.
Collate data relevant to each code.
See screenshot
3. Searching for themes.
Look at all the codes – are there similarities between them?
Similar codes would be put together into a theme.
4. Reviewing themes
Check the themes against the extracts you used in the last phase – do they go together to emphasise the point?
Remember this is the evidence for what you say, so it needs to be strong.
5. Defining and naming themes
Refine themes.
Check the themes tell a mini and overall story.
Generate clear definitions and names for each theme.
6. Producing the report
Present each new theme (titled) with a description of each.
Present the most vivid, compelling extract examples.
Drawback of Using Thematic Analysis
Very rarely reported.
Potential for weak and unconvincing analysis which don’t represent the data.
Mismatch between data and claims made about it – does evidence (quotes) represent themes adequately?

Using Content Analysis & Thematic Analysis
So we’ve learnt about these two analytical approaches…but when do we use each one?

Thematic Analysis: properly qualitative analysis (unlike content analysis).
Keeps the quality and integrity of the data.
Find out about the actual person.

Content Analysis: when you want properly valid and reliable qualitative (quantitative) analysis.
Perhaps good for mixed methods?
However, loses the essence of the data – so what’s the point in doing qualitative data collection?
Lecture Recap
Despite the apparently divisive nature of the qual v quant debate, there are numerous similarities between the two.
As in quantitative research, there are numerous data collection possibilities.
As in quantitative research, there are numerous data analysis possibilities.
Full transcript